


"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 UpperCAmelCase				=						argparse.ArgumentParser(
     description=(
         """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
         """ Distillation"""
     )
 )
 parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
 parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
 parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
 parser.add_argument("""--vocab_transform""", action="""store_true""")
 UpperCAmelCase				=						parser.parse_args()

 if args.model_type == "roberta":
  UpperCAmelCase				=						RobertaForMaskedLM.from_pretrained(args.model_name)
  UpperCAmelCase				=						"""roberta"""
 elif args.model_type == "gpt2":
  UpperCAmelCase				=						GPTaLMHeadModel.from_pretrained(args.model_name)
  UpperCAmelCase				=						"""transformer"""

 UpperCAmelCase				=						model.state_dict()
 UpperCAmelCase				=						{}

 # Embeddings #
 if args.model_type == "gpt2":
  for param_name in ["wte.weight", "wpe.weight"]:
   UpperCAmelCase				=						state_dict[f'''{prefix}.{param_name}''']
 else:
  for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
   UpperCAmelCase				=						f'''{prefix}.embeddings.{w}.weight'''
   UpperCAmelCase				=						state_dict[param_name]
  for w in ["weight", "bias"]:
   UpperCAmelCase				=						f'''{prefix}.embeddings.LayerNorm.{w}'''
   UpperCAmelCase				=						state_dict[param_name]

    # Transformer Blocks #
 UpperCAmelCase				=						0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  if args.model_type == "gpt2":
   for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
    for w in ["weight", "bias"]:
     UpperCAmelCase				=						state_dict[
         f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
     ]
   UpperCAmelCase				=						state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
  else:
   for layer in [
       "attention.self.query",
       "attention.self.key",
       "attention.self.value",
       "attention.output.dense",
       "attention.output.LayerNorm",
       "intermediate.dense",
       "output.dense",
       "output.LayerNorm",
   ]:
    for w in ["weight", "bias"]:
     UpperCAmelCase				=						state_dict[
         f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
     ]
  std_idx += 1

 # Language Modeling Head ###s
 if args.model_type == "roberta":
  for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
   UpperCAmelCase				=						state_dict[f'''{layer}''']
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    UpperCAmelCase				=						state_dict[f'''lm_head.dense.{w}''']
    UpperCAmelCase				=						state_dict[f'''lm_head.layer_norm.{w}''']
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   UpperCAmelCase				=						state_dict[f'''{prefix}.ln_f.{w}''']
  UpperCAmelCase				=						state_dict["""lm_head.weight"""]

 print(f'''N layers selected for distillation: {std_idx}''')
 print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

 print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
 torch.save(compressed_sd, args.dump_checkpoint)









"""simple docstring"""


import qiskit


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[Any]						=			qiskit.Aer.get_backend('aer_simulator' )

						# Create a Quantum Circuit acting on the q register
						lowerCAmelCase   :Optional[int]						=			qiskit.QuantumCircuit(a__     ,      a__ )

						# Apply X (NOT) Gate to Qubits 0 & 1
						circuit.x(0 )
						circuit.x(1 )

						# Map the quantum measurement to the classical bits
						circuit.measure([0, 1]     ,      [0, 1] )

						# Execute the circuit on the qasm simulator
						lowerCAmelCase   :Dict						=			qiskit.execute(a__     ,      a__     ,      shots=10_00 )

						# Return the histogram data of the results of the experiment.
						return job.result().get_counts(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   single_qubit_measure(2, 2)
		print(F"""Total count for various states are: {counts}""")